{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practice Lab: Neural Networks for Handwritten Digit Recognition, Binary\n",
    "\n",
    "In this exercise, you will use a neural network to recognize the hand-written digits zero and one.\n",
    "\n",
    "\n",
    "# Outline\n",
    "- [ 1 - Packages ](#1)\n",
    "- [ 2 - Neural Networks](#2)\n",
    "  - [ 2.1 Problem Statement](#2.1)\n",
    "  - [ 2.2 Dataset](#2.2)\n",
    "  - [ 2.3 Model representation](#2.3)\n",
    "  - [ 2.4 Tensorflow Model Implementation](#2.4)\n",
    "    - [ Exercise 1](#ex01)\n",
    "  - [ 2.5 NumPy Model Implementation (Forward Prop in NumPy)](#2.5)\n",
    "    - [ Exercise 2](#ex02)\n",
    "  - [ 2.6 Vectorized NumPy Model Implementation (Optional)](#2.6)\n",
    "    - [ Exercise 3](#ex03)\n",
    "  - [ 2.7 Congratulations!](#2.7)\n",
    "  - [ 2.8 NumPy Broadcasting Tutorial (Optional)](#2.8)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**NOTE:** To prevent errors from the autograder, you are not allowed to edit or delete non-graded cells in this notebook . Please also refrain from adding any new cells. \n",
    "**Once you have passed this assignment** and want to experiment with any of the non-graded code, you may follow the instructions at the bottom of this notebook._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"1\"></a>\n",
    "## 1 - Packages \n",
    "\n",
    "First, let's run the cell below to import all the packages that you will need during this assignment.\n",
    "- [numpy](https://numpy.org/) is the fundamental package for scientific computing with Python.\n",
    "- [matplotlib](http://matplotlib.org) is a popular library to plot graphs in Python.\n",
    "- [tensorflow](https://www.tensorflow.org/) a popular platform for machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "import matplotlib.pyplot as plt\n",
    "from autils import *\n",
    "%matplotlib inline\n",
    "\n",
    "import logging\n",
    "logging.getLogger(\"tensorflow\").setLevel(logging.ERROR)\n",
    "tf.autograph.set_verbosity(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tensorflow and Keras**  \n",
    "Tensorflow is a machine learning package developed by Google. In 2019, Google integrated Keras into Tensorflow and released Tensorflow 2.0. Keras is a framework developed independently by François Chollet that creates a simple, layer-centric interface to Tensorflow. This course will be using the Keras interface. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2\"></a>\n",
    "## 2 - Neural Networks\n",
    "\n",
    "In Course 1, you implemented logistic regression. This was extended to handle non-linear boundaries using polynomial regression. For even more complex scenarios such as image recognition, neural networks are preferred.\n",
    "\n",
    "<a name=\"2.1\"></a>\n",
    "### 2.1 Problem Statement\n",
    "\n",
    "In this exercise, you will use a neural network to recognize two handwritten digits, zero and one. This is a binary classification task. Automated handwritten digit recognition is widely used today - from recognizing zip codes (postal codes) on mail envelopes to recognizing amounts written on bank checks. You will extend this network to recognize all 10 digits (0-9) in a future assignment. \n",
    "\n",
    "This exercise will show you how the methods you have learned can be used for this classification task.\n",
    "\n",
    "<a name=\"2.2\"></a>\n",
    "### 2.2 Dataset\n",
    "\n",
    "You will start by loading the dataset for this task. \n",
    "- The `load_data()` function shown below loads the data into variables `X` and `y`\n",
    "\n",
    "\n",
    "- The data set contains 1000 training examples of handwritten digits $^1$, here limited to zero and one.  \n",
    "\n",
    "    - Each training example is a 20-pixel x 20-pixel grayscale image of the digit. \n",
    "        - Each pixel is represented by a floating-point number indicating the grayscale intensity at that location. \n",
    "        - The 20 by 20 grid of pixels is “unrolled” into a 400-dimensional vector. \n",
    "        - Each training example becomes a single row in our data matrix `X`. \n",
    "        - This gives us a 1000 x 400 matrix `X` where every row is a training example of a handwritten digit image.\n",
    "\n",
    "$$X = \n",
    "\\left(\\begin{array}{cc} \n",
    "--- (x^{(1)}) --- \\\\\n",
    "--- (x^{(2)}) --- \\\\\n",
    "\\vdots \\\\ \n",
    "--- (x^{(m)}) --- \n",
    "\\end{array}\\right)$$ \n",
    "\n",
    "- The second part of the training set is a 1000 x 1 dimensional vector `y` that contains labels for the training set\n",
    "    - `y = 0` if the image is of the digit `0`, `y = 1` if the image is of the digit `1`.\n",
    "\n",
    "$^1$<sub> This is a subset of the MNIST handwritten digit dataset (http://yann.lecun.com/exdb/mnist/)</sub>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "# load dataset\n",
    "X, y = load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"toc_89367_2.2.1\"></a>\n",
    "#### 2.2.1 View the variables\n",
    "Let's get more familiar with your dataset.  \n",
    "- A good place to start is to print out each variable and see what it contains.\n",
    "\n",
    "The code below prints elements of the variables `X` and `y`.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "deletable": false,
    "editable": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first element of X is:  [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  8.56059680e-06\n",
      "  1.94035948e-06 -7.37438725e-04 -8.13403799e-03 -1.86104473e-02\n",
      " -1.87412865e-02 -1.87572508e-02 -1.90963542e-02 -1.64039011e-02\n",
      " -3.78191381e-03  3.30347316e-04  1.27655229e-05  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  1.16421569e-04  1.20052179e-04\n",
      " -1.40444581e-02 -2.84542484e-02  8.03826593e-02  2.66540339e-01\n",
      "  2.73853746e-01  2.78729541e-01  2.74293607e-01  2.24676403e-01\n",
      "  2.77562977e-02 -7.06315478e-03  2.34715414e-04  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  1.28335523e-17 -3.26286765e-04 -1.38651604e-02\n",
      "  8.15651552e-02  3.82800381e-01  8.57849775e-01  1.00109761e+00\n",
      "  9.69710638e-01  9.30928598e-01  1.00383757e+00  9.64157356e-01\n",
      "  4.49256553e-01 -5.60408259e-03 -3.78319036e-03  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  5.10620915e-06\n",
      "  4.36410675e-04 -3.95509940e-03 -2.68537241e-02  1.00755014e-01\n",
      "  6.42031710e-01  1.03136838e+00  8.50968614e-01  5.43122379e-01\n",
      "  3.42599738e-01  2.68918777e-01  6.68374643e-01  1.01256958e+00\n",
      "  9.03795598e-01  1.04481574e-01 -1.66424973e-02  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.59875260e-05\n",
      " -3.10606987e-03  7.52456076e-03  1.77539831e-01  7.92890120e-01\n",
      "  9.65626503e-01  4.63166079e-01  6.91720680e-02 -3.64100526e-03\n",
      " -4.12180405e-02 -5.01900656e-02  1.56102907e-01  9.01762651e-01\n",
      "  1.04748346e+00  1.51055252e-01 -2.16044665e-02  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  5.87012352e-05 -6.40931373e-04\n",
      " -3.23305249e-02  2.78203465e-01  9.36720163e-01  1.04320956e+00\n",
      "  5.98003217e-01 -3.59409041e-03 -2.16751770e-02 -4.81021923e-03\n",
      "  6.16566793e-05 -1.23773318e-02  1.55477482e-01  9.14867477e-01\n",
      "  9.20401348e-01  1.09173902e-01 -1.71058007e-02  0.00000000e+00\n",
      "  0.00000000e+00  1.56250000e-04 -4.27724104e-04 -2.51466503e-02\n",
      "  1.30532561e-01  7.81664862e-01  1.02836583e+00  7.57137601e-01\n",
      "  2.84667194e-01  4.86865128e-03 -3.18688725e-03  0.00000000e+00\n",
      "  8.36492601e-04 -3.70751123e-02  4.52644165e-01  1.03180133e+00\n",
      "  5.39028101e-01 -2.43742611e-03 -4.80290033e-03  0.00000000e+00\n",
      "  0.00000000e+00 -7.03635621e-04 -1.27262443e-02  1.61706648e-01\n",
      "  7.79865383e-01  1.03676705e+00  8.04490400e-01  1.60586724e-01\n",
      " -1.38173339e-02  2.14879493e-03 -2.12622549e-04  2.04248366e-04\n",
      " -6.85907627e-03  4.31712963e-04  7.20680947e-01  8.48136063e-01\n",
      "  1.51383408e-01 -2.28404366e-02  1.98971950e-04  0.00000000e+00\n",
      "  0.00000000e+00 -9.40410539e-03  3.74520505e-02  6.94389110e-01\n",
      "  1.02844844e+00  1.01648066e+00  8.80488426e-01  3.92123945e-01\n",
      " -1.74122413e-02 -1.20098039e-04  5.55215142e-05 -2.23907271e-03\n",
      " -2.76068376e-02  3.68645493e-01  9.36411169e-01  4.59006723e-01\n",
      " -4.24701797e-02  1.17356610e-03  1.88929739e-05  0.00000000e+00\n",
      "  0.00000000e+00 -1.93511951e-02  1.29999794e-01  9.79821705e-01\n",
      "  9.41862388e-01  7.75147704e-01  8.73632241e-01  2.12778350e-01\n",
      " -1.72353349e-02  0.00000000e+00  1.09937426e-03 -2.61793751e-02\n",
      "  1.22872879e-01  8.30812662e-01  7.26501773e-01  5.24441863e-02\n",
      " -6.18971913e-03  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00 -9.36563862e-03  3.68349741e-02  6.99079299e-01\n",
      "  1.00293583e+00  6.05704402e-01  3.27299224e-01 -3.22099249e-02\n",
      " -4.83053002e-02 -4.34069138e-02 -5.75151144e-02  9.55674190e-02\n",
      "  7.26512627e-01  6.95366966e-01  1.47114481e-01 -1.20048679e-02\n",
      " -3.02798203e-04  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00 -6.76572712e-04 -6.51415556e-03  1.17339359e-01\n",
      "  4.21948410e-01  9.93210937e-01  8.82013974e-01  7.45758734e-01\n",
      "  7.23874268e-01  7.23341725e-01  7.20020340e-01  8.45324959e-01\n",
      "  8.31859739e-01  6.88831870e-02 -2.77765012e-02  3.59136710e-04\n",
      "  7.14869281e-05  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  1.53186275e-04  3.17353553e-04 -2.29167177e-02\n",
      " -4.14402914e-03  3.87038450e-01  5.04583435e-01  7.74885876e-01\n",
      "  9.90037446e-01  1.00769478e+00  1.00851440e+00  7.37905042e-01\n",
      "  2.15455291e-01 -2.69624864e-02  1.32506127e-03  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  2.36366422e-04\n",
      " -2.26031454e-03 -2.51994485e-02 -3.73889910e-02  6.62121228e-02\n",
      "  2.91134498e-01  3.23055726e-01  3.06260315e-01  8.76070942e-02\n",
      " -2.50581917e-02  2.37438725e-04  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  6.20939216e-18  6.72618320e-04 -1.13151411e-02\n",
      " -3.54641066e-02 -3.88214912e-02 -3.71077412e-02 -1.33524928e-02\n",
      "  9.90964718e-04  4.89176960e-05  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "  0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00]\n"
     ]
    }
   ],
   "source": [
    "print ('The first element of X is: ', X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first element of y is:  0\n",
      "The last element of y is:  1\n"
     ]
    }
   ],
   "source": [
    "print ('The first element of y is: ', y[0,0])\n",
    "print ('The last element of y is: ', y[-1,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"toc_89367_2.2.2\"></a>\n",
    "#### 2.2.2 Check the dimensions of your variables\n",
    "\n",
    "Another way to get familiar with your data is to view its dimensions. Please print the shape of `X` and `y` and see how many training examples you have in your dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (1000, 400)\n",
      "The shape of y is: (1000, 1)\n"
     ]
    }
   ],
   "source": [
    "print ('The shape of X is: ' + str(X.shape))\n",
    "print ('The shape of y is: ' + str(y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"toc_89367_2.2.3\"></a>\n",
    "#### 2.2.3 Visualizing the Data\n",
    "\n",
    "You will begin by visualizing a subset of the training set. \n",
    "- In the cell below, the code randomly selects 64 rows from `X`, maps each row back to a 20 pixel by 20 pixel grayscale image and displays the images together. \n",
    "- The label for each image is displayed above the image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1)\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(y[random_index,0])\n",
    "    ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.3\"></a>\n",
    "### 2.3 Model representation\n",
    "\n",
    "The neural network you will use in this assignment is shown in the figure below. \n",
    "- This has three dense layers with sigmoid activations.\n",
    "    - Recall that our inputs are pixel values of digit images.\n",
    "    - Since the images are of size $20\\times20$, this gives us $400$ inputs  \n",
    "    \n",
    "<img src=\"images/C2_W1_Assign1.PNG\" width=\"500\" height=\"400\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The parameters have dimensions that are sized for a neural network with $25$ units in layer 1, $15$ units in layer 2 and $1$ output unit in layer 3. \n",
    "\n",
    "    - Recall that the dimensions of these parameters are determined as follows:\n",
    "        - If network has $s_{in}$ units in a layer and $s_{out}$ units in the next layer, then \n",
    "            - $W$ will be of dimension $s_{in} \\times s_{out}$.\n",
    "            - $b$ will a vector with $s_{out}$ elements\n",
    "  \n",
    "    - Therefore, the shapes of `W`, and `b`,  are \n",
    "        - layer1: The shape of `W1` is (400, 25) and the shape of `b1` is (25,)\n",
    "        - layer2: The shape of `W2` is (25, 15) and the shape of `b2` is: (15,)\n",
    "        - layer3: The shape of `W3` is (15, 1) and the shape of `b3` is: (1,)\n",
    ">**Note:** The bias vector `b` could be represented as a 1-D (n,) or 2-D (1,n) array. Tensorflow utilizes a 1-D representation and this lab will maintain that convention. \n",
    "               "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.4\"></a>\n",
    "### 2.4 Tensorflow Model Implementation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensorflow models are built layer by layer. A layer's input dimensions ($s_{in}$ above) are calculated for you. You specify a layer's *output dimensions* and this determines the next layer's input dimension. The input dimension of the first layer is derived from the size of the input data specified in the `model.fit` statement below. \n",
    ">**Note:** It is also possible to add an input layer that specifies the input dimension of the first layer. For example:  \n",
    "`tf.keras.Input(shape=(400,)),    #specify input shape`  \n",
    "We will include that here to illuminate some model sizing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex01\"></a>\n",
    "### Exercise 1\n",
    "\n",
    "Below, using Keras [Sequential model](https://keras.io/guides/sequential_model/) and [Dense Layer](https://keras.io/api/layers/core_layers/dense/) with a sigmoid activation to construct the network described above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# UNQ_C1\n",
    "# GRADED CELL: Sequential model\n",
    "\n",
    "model = Sequential(\n",
    "    [               \n",
    "        tf.keras.Input(shape=(400,)),    #specify input size\n",
    "        ### START CODE HERE ### \n",
    "        Dense(units=25, activation=\"sigmoid\"),\n",
    "        Dense(units=15, activation=\"sigmoid\"),\n",
    "        Dense(units=1, activation=\"sigmoid\")\n",
    "        ### END CODE HERE ### \n",
    "    ], name = \"my_model\" \n",
    ")                            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"my_model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 25)                10025     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 15)                390       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 16        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 10,431\n",
      "Trainable params: 10,431\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Expected Output (Click to Expand) </b></font></summary>\n",
    "The `model.summary()` function displays a useful summary of the model. Because we have specified an input layer size, the shape of the weight and bias arrays are determined and the total number of parameters per layer can be shown. Note, the names of the layers may vary as they are auto-generated.  \n",
    "    \n",
    "    \n",
    "```\n",
    "Model: \"my_model\"\n",
    "_________________________________________________________________\n",
    "Layer (type)                 Output Shape              Param #   \n",
    "=================================================================\n",
    "dense (Dense)                (None, 25)                10025     \n",
    "_________________________________________________________________\n",
    "dense_1 (Dense)              (None, 15)                390       \n",
    "_________________________________________________________________\n",
    "dense_2 (Dense)              (None, 1)                 16        \n",
    "=================================================================\n",
    "Total params: 10,431\n",
    "Trainable params: 10,431\n",
    "Non-trainable params: 0\n",
    "_________________________________________________________________\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "As described in the lecture:\n",
    "    \n",
    "```python\n",
    "model = Sequential(                      \n",
    "    [                                   \n",
    "        tf.keras.Input(shape=(400,)),    # specify input size (optional)\n",
    "        Dense(25, activation='sigmoid'), \n",
    "        Dense(15, activation='sigmoid'), \n",
    "        Dense(1,  activation='sigmoid')  \n",
    "    ], name = \"my_model\"                                    \n",
    ")                                       \n",
    "``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "from public_tests import *\n",
    "\n",
    "test_c1(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameter counts shown in the summary correspond to the number of elements in the weight and bias arrays as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "L1 params =  10025 , L2 params =  390 ,  L3 params =  16\n"
     ]
    }
   ],
   "source": [
    "L1_num_params = 400 * 25 + 25  # W1 parameters  + b1 parameters\n",
    "L2_num_params = 25 * 15 + 15   # W2 parameters  + b2 parameters\n",
    "L3_num_params = 15 * 1 + 1     # W3 parameters  + b3 parameters\n",
    "print(\"L1 params = \", L1_num_params, \", L2 params = \", L2_num_params, \",  L3 params = \", L3_num_params )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can examine details of the model by first extracting the layers with `model.layers` and then extracting the weights with `layerx.get_weights()` as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "[layer1, layer2, layer3] = model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 shape = (400, 25), b1 shape = (25,)\n",
      "W2 shape = (25, 15), b2 shape = (15,)\n",
      "W3 shape = (15, 1), b3 shape = (1,)\n"
     ]
    }
   ],
   "source": [
    "#### Examine Weights shapes\n",
    "W1,b1 = layer1.get_weights()\n",
    "W2,b2 = layer2.get_weights()\n",
    "W3,b3 = layer3.get_weights()\n",
    "print(f\"W1 shape = {W1.shape}, b1 shape = {b1.shape}\")\n",
    "print(f\"W2 shape = {W2.shape}, b2 shape = {b2.shape}\")\n",
    "print(f\"W3 shape = {W3.shape}, b3 shape = {b3.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "```\n",
    "W1 shape = (400, 25), b1 shape = (25,)  \n",
    "W2 shape = (25, 15), b2 shape = (15,)  \n",
    "W3 shape = (15, 1), b3 shape = (1,)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`xx.get_weights` returns a NumPy array. One can also access the weights directly in their tensor form. Note the shape of the tensors in the final layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tf.Variable 'dense_2/kernel:0' shape=(15, 1) dtype=float32, numpy=\n",
      "array([[ 0.17990428],\n",
      "       [ 0.5710016 ],\n",
      "       [-0.49985638],\n",
      "       [ 0.25774735],\n",
      "       [ 0.0030514 ],\n",
      "       [ 0.31641507],\n",
      "       [ 0.13391268],\n",
      "       [-0.13130611],\n",
      "       [-0.60006225],\n",
      "       [ 0.10568619],\n",
      "       [ 0.23878062],\n",
      "       [ 0.08026403],\n",
      "       [-0.5279912 ],\n",
      "       [-0.11581469],\n",
      "       [ 0.10828614]], dtype=float32)>, <tf.Variable 'dense_2/bias:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>]\n"
     ]
    }
   ],
   "source": [
    "print(model.layers[2].weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code will define a loss function and run gradient descent to fit the weights of the model to the training data. This will be explained in more detail in the following week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "deletable": false,
    "editable": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "32/32 [==============================] - 1s 3ms/step - loss: 0.6416\n",
      "Epoch 2/20\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.5020\n",
      "Epoch 3/20\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.3526\n",
      "Epoch 4/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.2415\n",
      "Epoch 5/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.1728\n",
      "Epoch 6/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.1310\n",
      "Epoch 7/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.1038\n",
      "Epoch 8/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0854\n",
      "Epoch 9/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0718\n",
      "Epoch 10/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0618\n",
      "Epoch 11/20\n",
      "32/32 [==============================] - 0s 3ms/step - loss: 0.0539\n",
      "Epoch 12/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0478\n",
      "Epoch 13/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0429\n",
      "Epoch 14/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0388\n",
      "Epoch 15/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0355\n",
      "Epoch 16/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0327\n",
      "Epoch 17/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0303\n",
      "Epoch 18/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0283\n",
      "Epoch 19/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0265\n",
      "Epoch 20/20\n",
      "32/32 [==============================] - 0s 2ms/step - loss: 0.0249\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7b088b13d850>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(\n",
    "    loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
    ")\n",
    "\n",
    "model.fit(\n",
    "    X,y,\n",
    "    epochs=20\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the model on an example to make a prediction, use [Keras `predict`](https://www.tensorflow.org/api_docs/python/tf/keras/Model). The input to `predict` is an array so the single example is reshaped to be two dimensional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " predicting a zero: [[0.02541655]]\n",
      " predicting a one:  [[0.98782337]]\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(X[0].reshape(1,400))  # a zero\n",
    "print(f\" predicting a zero: {prediction}\")\n",
    "prediction = model.predict(X[500].reshape(1,400))  # a one\n",
    "print(f\" predicting a one:  {prediction}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the model is interpreted as a probability. In the first example above, the input is a zero. The model predicts the probability that the input is a one is nearly zero. \n",
    "In the second example, the input is a one. The model predicts the probability that the input is a one is nearly one.\n",
    "As in the case of logistic regression, the probability is compared to a threshold to make a final prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction after threshold: 1\n"
     ]
    }
   ],
   "source": [
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print(f\"prediction after threshold: {yhat}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the predictions vs the labels for a random sample of 64 digits. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "    \n",
    "    # Predict using the Neural Network\n",
    "    prediction = model.predict(X[random_index].reshape(1,400))\n",
    "    if prediction >= 0.5:\n",
    "        yhat = 1\n",
    "    else:\n",
    "        yhat = 0\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]},{yhat}\")\n",
    "    ax.set_axis_off()\n",
    "fig.suptitle(\"Label, yhat\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2.5\"></a>\n",
    "### 2.5 NumPy Model Implementation (Forward Prop in NumPy)\n",
    "As described in lecture, it is possible to build your own dense layer using NumPy. This can then be utilized to build a multi-layer neural network. \n",
    "\n",
    "<img src=\"images/C2_W1_dense2.PNG\" width=\"600\" height=\"450\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex02\"></a>\n",
    "### Exercise 2\n",
    "\n",
    "Below, build a dense layer subroutine. The example in lecture utilized a for loop to visit each unit (`j`) in the layer and perform the dot product of the weights for that unit (`W[:,j]`) and sum the bias for the unit (`b[j]`) to form `z`. An activation function `g(z)` is then applied to that result. This section will not utilize some of the matrix operations described in the optional lectures. These will be explored in a later section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "deletable": false,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# UNQ_C2\n",
    "# GRADED FUNCTION: my_dense\n",
    "\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "### START CODE HERE ### \n",
    "    for i in range(units):\n",
    "        w = W[:, i]\n",
    "        y = np.dot(w, a_in) + b[i]\n",
    "        a_out[i] = g(y)\n",
    "### END CODE HERE ### \n",
    "    return(a_out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.54735762 0.57932425 0.61063923]\n"
     ]
    }
   ],
   "source": [
    "# Quick Check\n",
    "x_tst = 0.1*np.arange(1,3,1).reshape(2,)  # (1 examples, 2 features)\n",
    "W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\n",
    "b_tst = 0.1*np.arange(1,4,1).reshape(3,)  # (3 features)\n",
    "A_tst = my_dense(x_tst, W_tst, b_tst, sigmoid)\n",
    "print(A_tst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "```\n",
    "[0.54735762 0.57932425 0.61063923]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "As described in the lecture:\n",
    "    \n",
    "```python\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "    for j in range(units):             \n",
    "        w =                            # Select weights for unit j. These are in column j of W\n",
    "        z =                            # dot product of w and a_in + b\n",
    "        a_out[j] =                     # apply activation to z\n",
    "    return(a_out)\n",
    "```\n",
    "   \n",
    "    \n",
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for more hints</b></font></summary>\n",
    "\n",
    "    \n",
    "```python\n",
    "def my_dense(a_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      a_in (ndarray (n, )) : Data, 1 example \n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j, )) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      a_out (ndarray (j,))  : j units\n",
    "    \"\"\"\n",
    "    units = W.shape[1]\n",
    "    a_out = np.zeros(units)\n",
    "    for j in range(units):             \n",
    "        w = W[:,j]                     \n",
    "        z = np.dot(w, a_in) + b[j]     \n",
    "        a_out[j] = g(z)                \n",
    "    return(a_out)\n",
    "``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "\n",
    "test_c2(my_dense)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell builds a three-layer neural network utilizing the `my_dense` subroutine above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "def my_sequential(x, W1, b1, W2, b2, W3, b3):\n",
    "    a1 = my_dense(x,  W1, b1, sigmoid)\n",
    "    a2 = my_dense(a1, W2, b2, sigmoid)\n",
    "    a3 = my_dense(a2, W3, b3, sigmoid)\n",
    "    return(a3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can copy trained weights and biases from Tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "W1_tmp,b1_tmp = layer1.get_weights()\n",
    "W2_tmp,b2_tmp = layer2.get_weights()\n",
    "W3_tmp,b3_tmp = layer3.get_weights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat =  0  label=  0\n",
      "yhat =  1  label=  1\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "prediction = my_sequential(X[0], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print( \"yhat = \", yhat, \" label= \", y[0,0])\n",
    "prediction = my_sequential(X[500], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "if prediction >= 0.5:\n",
    "    yhat = 1\n",
    "else:\n",
    "    yhat = 0\n",
    "print( \"yhat = \", yhat, \" label= \", y[500,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to see predictions from both the Numpy model and the Tensorflow model. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8,8, figsize=(8,8))\n",
    "fig.tight_layout(pad=0.1,rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i,ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20,20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "\n",
    "    # Predict using the Neural Network implemented in Numpy\n",
    "    my_prediction = my_sequential(X[random_index], W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "    my_yhat = int(my_prediction >= 0.5)\n",
    "\n",
    "    # Predict using the Neural Network implemented in Tensorflow\n",
    "    tf_prediction = model.predict(X[random_index].reshape(1,400))\n",
    "    tf_yhat = int(tf_prediction >= 0.5)\n",
    "    \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]},{tf_yhat},{my_yhat}\")\n",
    "    ax.set_axis_off() \n",
    "fig.suptitle(\"Label, yhat Tensorflow, yhat Numpy\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "<a name=\"2.6\"></a>\n",
    "### 2.6 Vectorized NumPy Model Implementation (Optional)\n",
    "The optional lectures described vector and matrix operations that can be used to speed the calculations.\n",
    "Below describes a layer operation that computes the output for all units in a layer on a given input example:\n",
    "\n",
    "<img src=\"images/C2_W1_VectorMatrix.PNG\" width=\"600\" height=\"450\">\n",
    "\n",
    "We can demonstrate this using the examples `X` and the `W1`,`b1` parameters above. We use `np.matmul` to perform the matrix multiply. Note, the dimensions of x and W must be compatible as shown in the diagram above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 25)\n"
     ]
    }
   ],
   "source": [
    "x = X[0].reshape(-1,1)         # column vector (400,1)\n",
    "z1 = np.matmul(x.T,W1) + b1    # (1,400)(400,25) = (1,25)\n",
    "a1 = sigmoid(z1)\n",
    "print(a1.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can take this a step further and compute all the units for all examples in one Matrix-Matrix operation.\n",
    "\n",
    "<img src=\"images/C2_W1_MatrixMatrix.PNG\" width=\"600\" height=\"450\">\n",
    "The full operation is $\\mathbf{Z}=\\mathbf{XW}+\\mathbf{b}$. This will utilize NumPy broadcasting to expand $\\mathbf{b}$ to $m$ rows. If this is unfamiliar, a short tutorial is provided at the end of the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"ex03\"></a>\n",
    "### Exercise 3\n",
    "\n",
    "Below, compose a new `my_dense_v` subroutine that performs the layer calculations for a matrix of examples. This will utilize `np.matmul()`.\n",
    "\n",
    "_**Note**: This function is not graded because it is discussed in the optional lectures on vectorization. If you didn't go through them, feel free to click the hints below the expected code to see the code. You can also submit the notebook even with a blank answer here._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "deletable": false
   },
   "outputs": [],
   "source": [
    "# UNQ_C3\n",
    "# UNGRADED FUNCTION: my_dense_v\n",
    "\n",
    "def my_dense_v(A_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      A_in (ndarray (m,n)) : Data, m examples, n features each\n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (1,j)) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      A_out (tf.Tensor or ndarray (m,j)) : m examples, j units\n",
    "    \"\"\"\n",
    "### START CODE HERE ### \n",
    "    y = np.matmul(A_in, W) + b\n",
    "    A_out = g(y)\n",
    "### END CODE HERE ### \n",
    "    return(A_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.54735762 0.57932425 0.61063923]\n",
      " [0.57199613 0.61301418 0.65248946]\n",
      " [0.5962827  0.64565631 0.6921095 ]\n",
      " [0.62010643 0.67699586 0.72908792]]\n"
     ]
    }
   ],
   "source": [
    "X_tst = 0.1*np.arange(1,9,1).reshape(4,2) # (4 examples, 2 features)\n",
    "W_tst = 0.1*np.arange(1,7,1).reshape(2,3) # (2 input features, 3 output features)\n",
    "b_tst = 0.1*np.arange(1,4,1).reshape(1,3) # (1,3 features)\n",
    "A_tst = my_dense_v(X_tst, W_tst, b_tst, sigmoid)\n",
    "print(A_tst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**\n",
    "\n",
    "```\n",
    "[[0.54735762 0.57932425 0.61063923]\n",
    " [0.57199613 0.61301418 0.65248946]\n",
    " [0.5962827  0.64565631 0.6921095 ]\n",
    " [0.62010643 0.67699586 0.72908792]]\n",
    " ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for hints</b></font></summary>\n",
    "    In matrix form, this can be written in one or two lines. \n",
    "    \n",
    "       Z = np.matmul of A_in and W plus b    \n",
    "       A_out is g(Z)  \n",
    "<details>\n",
    "  <summary><font size=\"3\" color=\"darkgreen\"><b>Click for code</b></font></summary>\n",
    "\n",
    "```python\n",
    "def my_dense_v(A_in, W, b, g):\n",
    "    \"\"\"\n",
    "    Computes dense layer\n",
    "    Args:\n",
    "      A_in (ndarray (m,n)) : Data, m examples, n features each\n",
    "      W    (ndarray (n,j)) : Weight matrix, n features per unit, j units\n",
    "      b    (ndarray (j,1)) : bias vector, j units  \n",
    "      g    activation function (e.g. sigmoid, relu..)\n",
    "    Returns\n",
    "      A_out (ndarray (m,j)) : m examples, j units\n",
    "    \"\"\"\n",
    "    Z = np.matmul(A_in,W) + b    \n",
    "    A_out = g(Z)                 \n",
    "    return(A_out)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[92mAll tests passed!\n"
     ]
    }
   ],
   "source": [
    "# UNIT TESTS\n",
    "\n",
    "test_c3(my_dense_v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell builds a three-layer neural network utilizing the `my_dense_v` subroutine above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "def my_sequential_v(X, W1, b1, W2, b2, W3, b3):\n",
    "    A1 = my_dense_v(X,  W1, b1, sigmoid)\n",
    "    A2 = my_dense_v(A1, W2, b2, sigmoid)\n",
    "    A3 = my_dense_v(A2, W3, b3, sigmoid)\n",
    "    return(A3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can again copy trained weights and biases from Tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "W1_tmp,b1_tmp = layer1.get_weights()\n",
    "W2_tmp,b2_tmp = layer2.get_weights()\n",
    "W3_tmp,b3_tmp = layer3.get_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a prediction with the new model. This will make a prediction on *all of the examples at once*. Note the shape of the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Prediction = my_sequential_v(X, W1_tmp, b1_tmp, W2_tmp, b2_tmp, W3_tmp, b3_tmp )\n",
    "Prediction.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll apply a threshold of 0.5 as before, but to all predictions at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predict a zero:  [0] predict a one:  [1]\n"
     ]
    }
   ],
   "source": [
    "Yhat = (Prediction >= 0.5).astype(int)\n",
    "print(\"predict a zero: \",Yhat[0], \"predict a one: \", Yhat[500])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to see predictions. This will use the predictions we just calculated above. This takes a moment to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "# You do not need to modify anything in this cell\n",
    "\n",
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(8, 8, figsize=(8, 8))\n",
    "fig.tight_layout(pad=0.1, rect=[0, 0.03, 1, 0.92]) #[left, bottom, right, top]\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    # Select random indices\n",
    "    random_index = np.random.randint(m)\n",
    "    \n",
    "    # Select rows corresponding to the random indices and\n",
    "    # reshape the image\n",
    "    X_random_reshaped = X[random_index].reshape((20, 20)).T\n",
    "    \n",
    "    # Display the image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "   \n",
    "    # Display the label above the image\n",
    "    ax.set_title(f\"{y[random_index,0]}, {Yhat[random_index, 0]}\")\n",
    "    ax.set_axis_off() \n",
    "fig.suptitle(\"Label, Yhat\", fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see how one of the misclassified images looks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 72x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(1, 1))\n",
    "errors = np.where(y != Yhat)\n",
    "random_index = errors[0][0]\n",
    "X_random_reshaped = X[random_index].reshape((20, 20)).T\n",
    "plt.imshow(X_random_reshaped, cmap='gray')\n",
    "plt.title(f\"{y[random_index,0]}, {Yhat[random_index, 0]}\")\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2.7\"></a>\n",
    "### 2.7 Congratulations!\n",
    "You have successfully built and utilized a neural network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<a name=\"2.8\"></a>\n",
    "### 2.8 NumPy Broadcasting Tutorial (Optional)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "In the last example,  $\\mathbf{Z}=\\mathbf{XW} + \\mathbf{b}$ utilized NumPy broadcasting to expand the vector $\\mathbf{b}$. If you are not familiar with NumPy Broadcasting, this short tutorial is provided.\n",
    "\n",
    "$\\mathbf{XW}$  is a matrix-matrix operation with dimensions $(m,j_1)(j_1,j_2)$ which results in a matrix with dimension  $(m,j_2)$. To that, we add a vector $\\mathbf{b}$ with dimension $(1,j_2)$.  $\\mathbf{b}$ must be expanded to be a $(m,j_2)$ matrix for this element-wise operation to make sense. This expansion is accomplished for you by NumPy broadcasting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Broadcasting applies to element-wise operations.  \n",
    "Its basic operation is to 'stretch' a smaller dimension by replicating elements to match a larger dimension.\n",
    "\n",
    "More [specifically](https://NumPy.org/doc/stable/user/basics.broadcasting.html): \n",
    "When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing (i.e. rightmost) dimensions and works its way left. Two dimensions are compatible when\n",
    "- they are equal, or\n",
    "- one of them is 1   \n",
    "\n",
    "If these conditions are not met, a ValueError: operands could not be broadcast together exception is thrown, indicating that the arrays have incompatible shapes. The size of the resulting array is the size that is not 1 along each axis of the inputs.\n",
    "\n",
    "Here are some examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center> <img src=\"./images/C2_W1_Assign1_BroadcastIndexes.PNG\"  alt='missing' width=\"400\"  ><center/>\n",
    "    <figcaption>Calculating Broadcast Result shape</figcaption>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphic below describes expanding dimensions. Note the red text below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <center> <img src=\"./images/C2_W1_Assign1_Broadcasting.gif\"  alt='missing' width=\"600\"  ><center/>\n",
    "    <figcaption>Broadcast notionally expands arguments to match for element wise operations</figcaption>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphic above shows NumPy expanding the arguments to match before the final operation. Note that this is a notional description. The actual mechanics of NumPy operation choose the most efficient implementation.\n",
    "\n",
    "For each of the following examples, try to guess the size of the result before running the example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(a + b).shape: (3, 1), \n",
      "a + b = \n",
      "[[6]\n",
      " [7]\n",
      " [8]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\n",
    "b = 5\n",
    "print(f\"(a + b).shape: {(a + b).shape}, \\na + b = \\n{a + b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this applies to all element-wise operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(a * b).shape: (3, 1), \n",
      "a * b = \n",
      "[[ 5]\n",
      " [10]\n",
      " [15]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3]).reshape(-1,1)  #(3,1)\n",
    "b = 5\n",
    "print(f\"(a * b).shape: {(a * b).shape}, \\na * b = \\n{a * b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<figure>\n",
    "    <img src=\"./images/C2_W1_Assign1_VectorAdd.PNG\"  alt='missing' width=\"740\" >\n",
    "    <center><figcaption><b>Row-Column Element-Wise Operations</b></figcaption></center>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [2]\n",
      " [3]\n",
      " [4]]\n",
      "[[1 2 3]]\n",
      "(a + b).shape: (4, 3), \n",
      "a + b = \n",
      "[[2 3 4]\n",
      " [3 4 5]\n",
      " [4 5 6]\n",
      " [5 6 7]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1,2,3,4]).reshape(-1,1)\n",
    "b = np.array([1,2,3]).reshape(1,-1)\n",
    "print(a)\n",
    "print(b)\n",
    "print(f\"(a + b).shape: {(a + b).shape}, \\na + b = \\n{a + b}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the scenario in the dense layer you built above. Adding a 1-D vector $b$ to a (m,j) matrix.\n",
    "<figure>\n",
    "    <img src=\"./images/C2_W1_Assign1_BroadcastMatrix.PNG\"  alt='missing' width=\"740\" >\n",
    "    <center><figcaption><b>Matrix + 1-D Vector</b></figcaption></center>\n",
    "<figure/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"2\" color=\"darkgreen\"><b>Please click here if you want to experiment with any of the non-graded code.</b></font></summary>\n",
    "    <p><i><b>Important Note: Please only do this when you've already passed the assignment to avoid problems with the autograder.</b></i>\n",
    "    <ol>\n",
    "        <li> On the notebook’s menu, click “View” > “Cell Toolbar” > “Edit Metadata”</li>\n",
    "        <li> Hit the “Edit Metadata” button next to the code cell which you want to lock/unlock</li>\n",
    "        <li> Set the attribute value for “editable” to:\n",
    "            <ul>\n",
    "                <li> “true” if you want to unlock it </li>\n",
    "                <li> “false” if you want to lock it </li>\n",
    "            </ul>\n",
    "        </li>\n",
    "        <li> On the notebook’s menu, click “View” > “Cell Toolbar” > “None” </li>\n",
    "    </ol>\n",
    "    <p> Here's a short demo of how to do the steps above: \n",
    "        <br>\n",
    "        <img src=\"https://lh3.google.com/u/0/d/14Xy_Mb17CZVgzVAgq7NCjMVBvSae3xO1\" align=\"center\" alt=\"unlock_cells.gif\">\n",
    "</details>"
   ]
  }
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